miceRanger: Multiple Imputation by Chained Equations with Random Forests

Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) <doi:10.1177/0962280206074463>. Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann <arXiv:1105.0828> to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.

Version: 1.3.5
Depends: R (≥ 3.5.0)
Imports: ranger, data.table, stats, FNN, ggplot2, crayon, corrplot, ggpubr, DescTools, foreach
Suggests: knitr, rmarkdown, doParallel, testthat (≥ 2.1.0)
Published: 2020-04-03
Author: Sam Wilson [aut, cre]
Maintainer: Sam Wilson <samwilson303 at gmail.com>
BugReports: https://github.com/FarrellDay/miceRanger/issues
License: MIT + file LICENSE
URL: https://github.com/FarrellDay/miceRanger
NeedsCompilation: no
Materials: NEWS
In views: MissingData
CRAN checks: miceRanger results


Reference manual: miceRanger.pdf
Vignettes: Diagnostic Plotting
The MICE Algorithm
Filling in Missing Data with miceRanger
Package source: miceRanger_1.3.5.tar.gz
Windows binaries: r-prerelease: miceRanger_1.3.5.zip, r-release: miceRanger_1.3.5.zip, r-oldrel: miceRanger_1.3.5.zip
macOS binaries: r-prerelease: miceRanger_1.3.5.tgz, r-release: miceRanger_1.3.5.tgz, r-oldrel: miceRanger_1.3.4.tgz
Old sources: miceRanger archive


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